Artículos relacionados a Business Analytics: Data Science for Business Problems

Business Analytics: Data Science for Business Problems - Tapa dura

Paczkowski, Walter R.

 
9783030870225: Business Analytics: Data Science for Business Problems
Tapa dura
ISBN 10: 3030870227 ISBN 13: 9783030870225
Editorial: Springer, 2022

Sinopsis

This book focuses on three core knowledge requirements for effective and thorough data analysis for solving business problems. These are a foundational understanding of:

1. statistical, econometric, and machine learning techniques;

2. data handling capabilities;

3. at least one programming language.

Practical in orientation, the volume offers illustrative case studies throughout and examples using Python in the context of Jupyter notebooks. Covered topics include demand measurement and forecasting, predictive modeling, pricing analytics, customer satisfaction assessment, market and advertising research, and new product development and research. This volume will be useful to business data analysts, data scientists, and market research professionals, as well as aspiring practitioners in business data analytics. It can also be used in colleges and universities offering courses and certifications in business data analytics, data science, and market research.


"Sinopsis" puede pertenecer a otra edición de este libro.

Acerca del autor

Walter R. Paczkowski, PhD, has worked at AT&T, AT&T Bell Labs, and AT&T Labs. He founded Data Analytics Corp., a statistical consulting company, in 2001. Dr. Paczkowski is also a part-time lecturer of economics at Rutgers University. He is the author of Deep Data Analytics for New Product Development (2020), Pricing Analytics: Models and Advanced Quantitative Techniques for Product Pricing (2018), and Market Data Analysis Using JMP (2016).


De la contraportada

This book focuses on three core knowledge requirements for effective and thorough data analysis for solving business problems. These are a foundational understanding of:

1. statistical, econometric, and machine learning techniques;

2. data handling capabilities;

3. at least one programming language.

 Practical in orientation, the volume offers illustrative case studies throughout and examples using Python in the context of Jupyter notebooks. Covered topics include demand measurement and forecasting, predictive modeling, pricing analytics, customer satisfaction assessment, market and advertising research, and new product development and research. This volume will be useful to business data analysts, data scientists, and market research professionals, as well as aspiring practitioners in business data analytics. It can also be used in colleges and universities offering courses and certifications in business data analytics, data science, and marketresearch.


"Sobre este título" puede pertenecer a otra edición de este libro.

Editorial
Springer
Año de publicación
2022
Idioma
Inglés
ISBN 10 
3030870227
ISBN 13 
9783030870225
Encuadernación
Tapa dura
Número de edición
1
Número de páginas
428
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